Method and system for statistical comparison of a plurality of testers

ABSTRACT

The invention provides methods and systems for statistically comparing yields among two or more testers in a testing environment where a lot of manufactured articles such as semiconductor wafers are randomly divided among and independently tested by the testers. Generally, in real-time, tests are performed on the lot and yield statistics are determined for each of the two or more testers as a function of test yields and the number of tests performed by each tester. Using the yield statistics, a univariate T-statistic for each tester is determined and serves as the basis for comparing each tester with a predetermined threshold statistical range.

CLAIM TO PRIORITY

[0001] This application claims the benefit of U.S. ProvisionalApplication No. 60/344,198, filed Dec. 28, 2001.

TECHNICAL FIELD

[0002] The present invention relates in general to real-time statisticalanalysis of testing of a manufactured product lot split among multipletesters. More particularly, the invention relates to comparing theresults of two or more testers for an entire lot of semiconductor wafersusing real-time statistical analysis of test data obtained duringtesting.

BACKGROUND OF THE INVENTION

[0003] No manufacturing process, even at concerns such as TexasInstruments, is so perfect that all products are completely alike. Thereare generally variations that may be caused by a great number of small,uncontrollable factors, and must, therefore, be regarded as a chancevariation.

[0004] In general, however, it is desirable to ensure that manufacturedarticles possess characteristics that fall within a required range ofvalues. Such characteristics may define the operating conditions andperformance characteristics of the device necessary for complianceverification. For this purpose, it is useful to make tests of thehypothesis that the products possess the required characteristics. Iftesting is done after an entire lot has been produced, for example, alot of 24 semi-conductor wafers, each wafer containing hundreds, or evenhundreds of thousands of circuits, the testing will reveal how closelythe lot of wafers approach the desired characteristics. In this way,products that do not meet rigorous testing standards can be identifiedand intercepted before they are passed on to customers and productionproblems can be addressed. This type of testing is common in the art.

[0005] One problem with such a testing approach is that considerabletime and testing resources may be used in testing a lot that isultimately rejected. It would be advantageous in terms of cost andefficiency to identify problem products within a lot as early aspossible in the testing cycle. A further problem is encountered,however, if the testing process is stopped too liberally. Stopping thetesting process even though it is progressing properly is known as aType I error. Not stopping a process even though something is amiss isknown as a Type II error. Thus, either erroneously interrupting orfailing to interrupt testing of a lot of manufactured articles canpresent problems in the art. These and other problems can be traced topotential invalidity of test data and the relative differences in testresults obtained for one tester versus another tester.

[0006] With reference to the manufacturing of semiconductor wafers, itis well known in the industry to use electronic testers that havefingers that touch bond pads of a chip. Typically, anywhere from 100 to1,000 bond pads are touched by the fingers, such that a program is ableto access the memory cells of the semiconductor device in order toanalyze its performance. Since a lot of semiconductor wafers is splitamong testers, randomization ensures that wafers from a contaminated lotare split among testers in a fairly even way. Therefore, by the time thelot reaches the tester, any anomalies in the test data can becontributed to failures in a tester or decreased performance by aparticular tester.

[0007] Thus, since post-process randomization ensures that contaminatedlots are split among multiple testers, a ready means fo ensuring testerperformance and identifying problems is required. One prior method ofverifying tester performance is known as ANOVA (analysis of variance).ANOVA is technique that uses analysis of variance to statisticallycompare the means among groups of test data. In order for ANOVA to beeffective, it is usually required to be run for an entire manufacturingcycle in order to collect enough test data for the ANOVA analysis.Moreover, with the ANOVA procedure, a single statistical quantity foreach split of a lot is not obtained, making it difficult to isolate anyparticular tester in terms of its performance versus a required standardor threshold. Therefore, with the ANOVA process, one only obtains thedifference between one tester and another, and not an actual figure foreach individual tester. This leaves the technician without confidencethat any one tester is operating within a certain tolerance or that aparticular tester has a problem.

[0008] Another disadvantage of prior art tester verification processesis the inability to identify problems in real-time. Typically, an entiretesting cycle has to be completed before sufficient data can be obtainedfor these required statistical analyses. This method, however, isinefficient and costly in a real life manufacturing environment.

[0009] Therefore, it would be useful and advantageous to provide a meansof minimizing tester errors by identifying problems with one of any of aplurality of testers for which a lot of manufactured devices has beensplit. Such a means would be useful if it could be applied in real-timeand if it could produce some qualitative indicator of the tester'sperformance.

SUMMARY OF THE INVENTION

[0010] In general, the present invention provides novel methods andsystems for real-time statistical comparison of a plurality of testerstesting a randomly divided lot of manufactured articles such assemiconductor wafers.

[0011] According to one exemplary embodiment of the invention, disclosedfor use in a semiconductor wafer testing environment is a method ofstatistically comparing yields among two or more testers. The methodcomprises the step of randomly dividing a produced lot of wafers amongthe two or more testers. Next, yield statistics are determined for eachof the two or more testers as a function of test yields and the numberof tests performed on the lot by each tester. Using the yieldstatistics, a univariate T-statistic for each tester is determined andis used as the basis for comparing each tester with a predeterminedthreshold statistical range.

[0012] According to one aspect of the invention, the method includes thestep of taking corrective action with respect to each tester associatedwith a univariate T-statistic outside the predetermined thresholdstatistical range.

[0013] According to a further aspect of the invention, method steps aretaken in order to provide a Type-I error probability of approximately0.05.

[0014] According to yet another embodiment of the invention, a system isdisclosed that has multiple testers for independently performing testsand generating test data relating to articles selected from amanufactured lot. One or more computers coupled to the testers havesoftware for determining yield statistics as a function of real-timetest data and a count of the tests performed by each tester. Thecomputer and software include capabilities for determining a univariateT-statistic for each tester, and for comparing the univariateT-statistic of each tester with a predetermined threshold statisticalrange.

[0015] According to still another aspect of the invention, the testersof the system are adapted to test a plurality of test dice disposed onsemiconductor wafers.

[0016] According to another aspect of the invention, the system includesmeans for taking corrective action with respect to each testerassociated with a univariate T-statistic outside the predeterminedthreshold statistical range.

[0017] According to yet another aspect of the invention, the systemincludes a distributed computer network coupled to the testers.

[0018] Further disclosed is a software-based program product withmachine readable instructions executable by a computer. The programproduct can be used in making real-time statistical comparisons of twoor more testers used in testing a randomly divided lot of manufacturedarticles. Included are instructions for maintaining a count of testsperformed by each of the testers, and for monitoring test data generatedby each of the testers. The instructions also include capabilities fordetermining yield statistics for each tester as a function of test countand test data. Also included are instructions for calculating aunivariate T-statistic for each tester and for comparing each univariateT-statistic with a predetermined threshold statistical confidence level.

[0019] According to yet another aspect of the invention, the machinereadable instructions include controlling means for taking correctiveaction with respect to each tester associated with a univariateT-statistic outside the predetermined threshold statistical range.

[0020] According to further additional aspects of the invention, themachine readable instructions may reside on magnetic, optical, orelectronic storage media.

[0021] The invention provides several technical advantages including,but not limited to, enabling a real-time response to statisticalanalysis and comparison of the performance of a plurality of testers.Thus, the systems and methods of the invention provide substantialsavings of time and expense associated with such testing. A furtheradvantage is provided by aspects of the invention which permit thetimely recognition of testing errors so that corrective action may betaken while minimizing the potential for Type I errors, or “falsealarms”, concerning the validity of the testing.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and additional features and advantages of the presentinvention will be more clearly understood from consideration of thefollowing detailed description in connection with the accompanyingdrawings in which:

[0023]FIG. 1 is an illustration of an example of a preferred embodimentof a system of the invention;

[0024]FIG. 2 is a graphical representation of defects on a wafer;

[0025]FIG. 3 is a process flow diagram of a method of computing yieldstatistics for use according to the invention;

[0026]FIG. 4 is a graphical representation of a probability densityfunction known in the art; and

[0027]FIG. 5 is a process flow diagram illustrating the steps in anexample of a preferred embodiment of the invention.

[0028] References in the detailed description correspond to likereferences in the figures unless otherwise noted. The descriptive anddirectional terms used in the written description such as top, bottom,left, right, etc., refer to the drawings themselves as laid out on thepaper and not to physical limitations of the invention unlessspecifically noted. The drawings are not to scale and some features ofembodiments shown and discussed are simplified or exaggerated forillustrating the principles of the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0029]FIG. 1 depicts an example implementation of the invention in theform of a semi-conductor wafer testing system 10. A lot of wafers 12 ismade up of individual wafers 14 which are randomly split for testing ontwo or more testers, such as tester 16 and tester 18. Each tester 16, 18is coupled to one or more computer(s) 20. Of course it will be apparentto those skilled in the art that the number of testers, type, number andconfiguration of computers and other details of the testing environmentmay be varied without altering the concept of the invention. In thepresently preferred embodiment, the computers 20 are networked providingdata and software sharing capabilities.

[0030] In general, each time a wafer 14 is tested, for example wafer14A, a check is made to determine whether at least two wafers 14 fromthis particular lot 12 have been tested on this particular tester, inthis example tester 16, and that at least two wafers from this lot 12have been tested on some other tester, in this case, tester 18. Assumingfor the sake of this example that this is so and that tester 16 hastested at least two wafers and tester 18 has tested at least two wafers.Yields statistics 17 relating to testers 16 and 18, and the waferstested thereon, are compiled. The average yield per wafer for eachtester is readily determined by adding the individual wafer yields anddividing by the number of wafers tested. The average yield per lot isalso determined by averaging the wafer yields for all wafers in the lot.

[0031]FIG. 2 is a graphical representation of defective dice 13 on awafer 14. Defective dice 13 are shown by the shaded areas and may belocated at focal points or randomly on the wafer 14. Non-defective dice15 are indicated by the unshaded portions.

[0032] Preferably, the corrected yield per wafer, also denominatedherein as delta yield, ΔY, is computed by subtracting the yield per lotfrom the yield per wafer. This relationship is governed by Equation (1)below:

ΔY=Y _(wafer) −Y _(lot)   Equation (1)

[0033] Thus, as shown in FIG. 3 and further described below, one deltayield value is preferably computed 110 for each wafer tested. Theaverage 112, standard deviation 114, and count 116 of wafers tested canthen be computed. Typically the tester having the largest average deltayield and the tester having the smallest average delta yield are thenidentified 118.

[0034] Using the yield statistics 17, a univariate T statistic may becalculated for each tester according to Equation (2): $\begin{matrix}{{T\left( {\Delta \quad Y} \right)} = \frac{{average}\quad \left( {\Delta \quad Y} \right) \times {sqrt}\left\{ {{N\left( {\Delta \quad Y} \right)} - 1} \right\}}{s\left( {\Delta \quad Y} \right)}} & {{Equation}\quad (2)}\end{matrix}$

[0035] Typically, this produces a T-distribution with N(ΔY)−1 degrees offreedom. Thus, a probability associated with this T-score can beobtained by integrating from negative infinity up to T(ΔY) using theprobability density function f(T) of a T-distribution with N(ΔY)−1degrees of freedom. This probability is a cumulative probabilityassociated with the T-score for each tester. Using this data, thesystems and methods of the invention individually evaluate the resultsobtained from each tester, making possible a comparison between thetester results and the probability of obtaining such results. Agraphical representation of a typical probability density function isshown in FIG. 4.

[0036] An example of the invention is now described in general, followedby an additional description with reference to FIG. 5. In the preferredembodiment of the invention, if the probability of given T-score ofgiven tester results is less than about 0.025, the tester associatedwith the low probability T-score may be producing an abnormally lowyield. Accordingly, each tester having an associated T-score giving aprobability of less than 0.025 is preferably subjected to correctiveaction. In this example, the probability of 0.025 is used in order tohelp minimize Type I errors. Of course, it will be understood by thosefamiliar with the art that a different threshold value may be usedaccording to the Type I error tolerance and other considerationsrelating to the particular testing procedure. In this instance thecorrective action taken is to immediately take the tester offline andattempt to identify the source of the apparent abnormally low yield.

[0037] On the other hand, a probability associated with a particulartester T-statistic of greater than 0.975 would indicate that theassociated tester is most likely producing an abnormally high yield. Inthe present example of the invention, this would necessitate correctiveaction in the form of taking offline every tester currently testingwafers from the same lot from which the apparently abnormally highyielding tester is also testing wafers. Preferably, however, the probingof a particular wafer is not interrupted, but rather, each of thetesters engaged in probing a wafer from the effected lot is permitted tocomplete testing its current wafer before being taken offline forfurther corrective action. Of course, in other testing applications ortesting environments the probability value for taking corrective actionbecause of an apparent abnormally good yield could be higher or lower.

[0038] In this example, a probability between 0.025 and 0.975 indicatesthat the tester associated with such a probability value is performingits function adequately and it is then allowed to continue on and testthe next wafer. It should be understood that each of the testers musthave an associated probability within the predetermined acceptablerange, in this particular case between 0.025 and 0.975, in order to beused for continued testing. Of course, it will also be apparent to thoseskilled in the arts that the above described systems and methods may beadvantageously implemented by means of computer software accessible tocomputers coupled to the testers, either individually or through anetwork.

[0039]FIG. 5 is a process flow diagram illustrating a method of theinvention. As shown in step 100, a tester, herein designated tester “X”to indicate that the step applies to each and every tester involved inthe testing of the current lot, completes testing of an N^(th) wafer. Asshown by decision diamond 102, if “N” is not greater than or equal to 2,arrow path 104 is followed and tester “X” tests another wafer.Otherwise, process flow is directed to step 108. If tester “X” hascompleted testing the second or a subsequent wafer, the average yield oftester “X” is computed and shown in step 108.

[0040] Continuing to step 110, a delta yield is then computed for tester“X”. Subsequently, as shown by steps 112, 114 and 116, the average deltayield for tester “X,” standard deviation of the delta yield of tester“X” and number of tests performed by tester “X” may then be computed.

[0041] A T-score for the average delta yield of tester “X” is thendetermined as shown in step 120 and, as indicated by decision diamond122, is checked against the lower confidence limit of the particularT-score, in this case 0.025 as indicated by arrow path 124. The T-scoreis also checked against the upper confidence limit in decision diamond126, as shown by arrow path 124. If the probability of the T-scorecomputed for tester “X” is within acceptable confidence limits, themethod proceeds back to step 100 and tests the next wafer. In the eventthat the T-score probability of tester “X” proves to be below the lowerconfidence limit or above the upper confidence limit, corrective actionis taken so that the associated tester, in this case tester “X,” is shutdown, step 132 or step 136.

[0042] Returning to decision diamond 126, in the event that theprobability of the T-score of tester “X” is above the range ofacceptable probability values, in this case 0.975, arrow path 134 isfollowed and all testers associated with the lot being tested by tester“X” and its companion testers, in this case testers “Y” and “Z”, areshut down, step 136. As described above, step 136 may also includefurther steps for allowing testers “Y” and “Z” to complete testing oftheir current wafers upon which testing is in progress.

[0043] Thus, the systems and methods shown and described, and thepossible alternative embodiments, may be used to evaluate the test dataproduced by two or more testers performing tests on a randomly dividedlot. Assuming the initial condition of N=2 is met, the systems andmethods of the invention may be reiterated, in principle indefinitely,in order to compare individual tester data with statistical predictions.

[0044] With reference to the following tables, a numerical example isprovided to further illustrate the method of the invention. In thefollowing numerical example, 12 wafers are randomly distributed betweentwo testers for yield testing. Tester A tests 7 wafers and Tester Btests 7 wafers, thus, N=7. This example is simplified for convenience inexplaining and understanding the invention. In practice of course,larger lots, a larger number of testers, and an unequal number of waferstested by each tester may be used.

[0045] Suppose we start with 14 wafers run in a row: time when wafertester completes yield number a 11:02 61.7 1 b 11:04 65.1 2 a 11:06 59.83 b 11:08 64.4 4 a 11:10 60.1 5 b 11:12 63.7 6 a 11:14 62.1 7 b 11:1668.8 8 a 11:18 61.8 9 b 11:20 68 10 a 11:22 63.7 11 b 11:24 67.1 12 a11:26 61.5 13 b 11:28 65.2 14

[0046] After the first 4 wafers have finished, we observe the followingstatistics:

[0047] For split A: average delta yield −2 number of wafers 2 standarddeviation 1.343503 t −1.48865 probability 0.188285

[0048] For split B: average delta yield 2 number of wafers 2 standarddeviation 0.494975 t 4.04061 probability 0.922774

[0049] Since 0.188 is larger than 0.025 AND 0.922774 is smaller than0.975, we do not stop the testers.

[0050] In likewise manner, after testing 5 wafers, we also do not stopthe testers, as the statistics are: split A Split B average delta yield−1.68667 average delta yield 2.53 number of wafers 3 number of wafers 2standard deviation 1.021437 standard deviation 0.494975 t −2.33525 t5.111372 probability 0.072313 probability 0.938502

[0051] In this example, it is only after we have completed 8 wafers thatwe finally decide to stop the testers. The statistics are: split A splitB average delta yield −2.2875 average delta yield 2.2875 number ofwafers 4 number of wafers 4 standard deviation 1.144188 standarddeviation 2.27303 t −3.46277 t 1.743077 probability 0.020279 probability0.910162

[0052] The idea is that we finally have 0.02 which is less than 0.025.If we had run all 14 wafers from this example, then we would observe thefollowing statistics: split A split B average delta yield −2.25714average delta yield 2.257143 number of wafers 7 number of wafers 7standard deviation 1.302196 standard deviation 1.929471 t −4.24579 t2.865474 probability 0.002703 probability 0.985702

[0053] As can be seen from the tables above, seven yield results havebeen obtained for each of testers A and B. The Yield per Lot is based onall results from all tests performed on the lot thus far, i.e.; N=7. Itshould be appreciated that each tester may be evaluated at any time aslong as N is equal to or greater than 2. Preferably the priorperformance of each tester is evaluated as the tester engages in testingits next wafer.

[0054] Beginning with tester A, the yield statistics are compiled asreflected in the table. Delta yield (Δ Yield A) values are obtained bysubtracting the average yield per lot from the yield per wafer. Themean, standard deviation and variance are then calculated as known inthe arts. The univariate T-statistic, or T-score, for Tester A is thencomputed, as illustrated by Equation 2 above.

[0055] It is assumed that the data is Normally distributed. Theprobability density function of the T distribution may be obtained bymeans of a look-up table or integration. The cumulative probabilityvalue thus obtained is then compared with the preselected range ofacceptable probability values. If the yields obtained by Tester A arefound to be improbably high or low, the appropriate corrective action istaken. In this example, referring to the probability density functiondepicted in FIG. 4, the T-score associated with tester A has aprobability of about 0.04, which is within the assumed acceptable rangeof greater than 0.025 and less than 0.975. Accordingly, no correctiveaction is taken with respect to tester A, and tester A continues testingthe next wafer (N=7, not shown). At the completion of, and during thetesting of the wafer, the method may be repeated. Of course, the processmay be repeated indefinitely.

[0056] The probability density function of the T distribution isconsulted by means of a look-up table or integration. The cumulativeprobability value thus obtained is compared with the preselected rangeof acceptable probability values. If the yield obtained by Tester B isimprobably high or low, the appropriate corrective action can be taken.In this example, after eight wafers have been tested, the T-scoreassociated with Tester B, for 3 degrees of freedom (N−1), has aprobability of 0.02, which, referring to the probability densityfunction of FIG. 4, is outside of the acceptable range of 0.025 and0.975. Accordingly, corrective action is taken with respect to Tester B.For example, assuming testing of a fifth wafer (N=5) is in progress,preferably Tester B will be taken offline and investigated for itsapparently abnormally high yield when testing of wafer 5 is completed.Tester A will be taken offline as well to prevent Type II errors in theevent the Tester B results are in fact correct.

[0057] Thus, by applying statistical analysis and making a statisticalcomparison of the performance of two or more testers independentlyperforming tests on water randomly split for a lot among the testers,the invention provides real-time comparison of the performance of anindividual tester. Thereby, the invention also provides timely andreal-time recognition of testing errors so that corrective action may betaken. Moreover, contrary to the prior art, the present inventionprovides a measure of performance for a particular tester as opposed torelative statistics for a group of testers.

[0058] While the invention has been described with reference toillustrative embodiments, this description is not intended to beconstrued in a limiting sense. Various modifications and combinations ofthe illustrative embodiments as well as other embodiments of theinvention will be apparent to persons skilled in the art upon referenceto the description and claims.

What is claimed is:
 1. In a wafer testing environment, a method ofstatistically comparing yields among two or more testers comprising thesteps of: for a plurality of wafers comprising a lot, randomly dividingthe lot among the two or more testers; for each of the two or moretesters, determining yield statistics as a function of real-time wafertest yields and the number of wafer tests performed on the lot by eachtester; and using the yield statistics, determining a univariateT-statistic for each tester for comparing the univariate T-statistic foreach tester with a predetermined threshold statistical range.
 2. Themethod of claim 1 wherein the step of determining yield statistics foreach tester further comprises the steps of: computing an averagecorrected yield, avg(Δy), associated with each tester by therelationship, avg(Δy)=y_(wafer)−y_(lot); wherein y_(wafer) is the yieldper wafer and y_(lot) is the yield per lot.
 3. The method of claim 2wherein the step of determining a univariate T-statistic for each testerfurther comprises the steps of: computing a univariate T-statistic forthe average corrected yield of each tester; and computingT(Δy)=avg(Δy)*sqrt{n(Δy)−1}/s(Δy); wherein s(Δy) is the standarddeviation associated with the avg(Δy) and n is the number of testsperformed by an associated tester.
 4. The method of claim 1 furthercomprising the step of taking corrective action with respect to eachtester associated with a univariate T-statistic outside thepredetermined threshold statistical range.
 5. The method of claim 4wherein the step of taking corrective action further comprises the stepof taking offline each tester associated with a univariate T-statisticbelow the predetermined threshold statistical range.
 6. The method ofclaim 4 wherein the step of taking corrective action further comprisesthe step of: for a lot in which a univariate T-statistic associated withone or more tester is above the predetermined threshold statisticalrange, taking offline each tester testing the lot.
 7. The method ofclaim 6 further comprising the step of permitting each tester tocomplete a test in progress prior to taking the tester offline.
 8. Themethod according to claim 1 wherein the threshold range of univariateT-statistic values consists of probability within the range of about0.025 to about 0.975.
 9. The method according to claim 1 wherein thethreshold statistical range is selected in order to provide a Type-Ierror probability of approximately 0.05.
 10. A system for statisticallycomparing two or more testers testing a randomly divided lot ofmanufactured articles, comprising: a plurality of testers forindependently performing tests on articles selected from a manufacturedlot and generating test data; one or more computer coupled to theplurality of testers; software executable by the one or more computer,the software further comprising; means for determining yield statisticsas a function of real-time test data and a count of the tests performedon the lot by each tester; means for determining a univariateT-statistic for each tester; and means for comparing the univariateT-statistic of each tester with a predetermined threshold statisticalrange.
 11. The system of claim 10 wherein the testers are adapted totest a plurality of test dice on semiconductor wafers.
 12. The system ofclaim 10 further comprising controlling means for taking correctiveaction with respect to each tester associated with a univariateT-statistic outside the predetermined threshold statistical range. 13.The system of claim 10 wherein the one or more computers comprise adistributed network coupled to the plurality of testers.
 14. Machinereadable instructions executable by a computer for use in makingreal-time statistical comparisons of two or more testers testing arandomly divided lot of manufactured articles, comprising: means formaintaining a count of tests performed by each of the two or moretesters; means for monitoring test data generated by each of the two ormore testers; means for determining yield statistics as a function oftest count and test data for each tester; means for calculating aunivariate T-statistic for each tester; and means for comparing eachunivariate T-statistic with a predetermined threshold statistical range.15. Machine readable instructions according to claim 14 wherein the testdata relates to a plurality of test dice on a semiconductor wafer. 16.The machine readable instructions of claim 14 further comprisingcontrolling means for taking corrective action with respect to eachtester associated with a univariate T-statistic outside thepredetermined threshold statistical range.
 17. The machine readableinstructions of claim 15 wherein the controlling means further compriseinstructions for taking offline each tester associated with a univariateT-statistic below the predetermined threshold statistical range.
 18. Themachine readable instructions of claim 15 wherein the controlling meansfurther comprise instructions for taking offline each tester testing alot for which a univariate T-statistic associated with one or moretester is above the predetermined threshold statistical range.
 19. Themachine readable instructions of claim 18 further comprisinginstructions for permitting each tester to complete a test in progressprior to taking the tester offline.
 20. The machine readableinstructions of claim 14 further comprising a magnetic storage medium.21. The machine readable instructions of claim 14 further comprising anoptical storage medium.
 22. The machine readable instructions of claim14 further comprising an electronic storage medium.
 23. The machinereadable instructions of claim 14 wherein the means for determiningyield statistics for each tester further comprises instructions for:computing an average corrected yield, avg(Δy), associated with eachtester by the relationship, avg(Δy)=y_(test)−y_(lot); wherein y_(test)is the yield per tested article and y_(lot) is the yield per lot. 24.The machine readable instructions of claim 14 wherein the means fordetermining a univariate T-statistic for each tester further comprisesinstructions for: computing a univariate T-statistic for the averagecorrected yield of each tester; and computingT(Δy)=avg(Δy)*sqrt{n(Δy)−1}/s(Δy); wherein s(Δy) is the standarddeviation associated with the avg(Δy) and n is the count of testsperformed by an associated tester.
 25. The machine readable instructionsaccording to claim 14 wherein the threshold range of univariateT-statistics consists of probability within the range of about 0.025 toabout 0.975.
 26. The machine readable instructions according to claim 14wherein the threshold statistical range is selected in order to providea Type-I error probability of approximately 0.05.